Detecting, segmenting and classifying objects and other structures of interest in a background retinal, and even any other medical, image is crucial for detection and diagnosis of disease, its absence, as well as for biometrics. Deep neural networks (NNs), including Convolutional Neural Networks, as well as other types of multilevel neural networks, are an existing method for improved feature learning, classification, and detection. They have been applied to a wide range of different image types, including medical images). However, the straightforward use of NNs to classify entire retinal images or image subsets (patches) for these tasks, as is the art in other problems, may not lead to maximized performance in retinal images. This is because the problem is underconstrained: there is a large variance in color, structure, and texture of the normal retina as well as a large diversity of objects, and a large variability in shapes, colors, textures, and other features of these objects, versus a relatively sparse number of retinal images with annotations at a sufficient level, that these machine learning algorithms require for optimal performance. Medical images with annotations are sparse and expensive, because it both ultimately derives from patients so there are ethical concerns that prevent an unlimited number of images to be obtained, as well annotating these images which requires experts in that field, in contrast with other domains especially in computer vision where NNs are typically can be trained on unlimited number of images can be obtained and annotation can typically be done by any adult.
To tackle this, other approaches have introduced additional variance into the object samples by translating, rotating and otherwise deforming patches or samples artificially. However, this still does not introduce sufficient ‘real’ variance, in both objects, as well as backgrounds. Accordingly, there is a need in the art for a process for creating, training and applying NNs to retinal images in order to maximally successfully detect objects in backgrounds related to disease detection in a way that is useful for retinal disease detection in patients.